Case Studies

The current versions of Adaptive Driving Beams available in Japan and Europe are using traditional machine vision techniques like template matching. Those machine learning techniques perform very well in constrained environments but are prone to many false positive when used in the dynamic, real world, with a lot more variations in targets and backgrounds.

There are several factors simultaneously driving integration of AI in radiology. Firstly, in many countries around the world there is a discrepancy between the number of doctors trained in radiology and the rising demand for diagnostic imaging. This leads to greater demands for work efficiency and productivity. For example, the number of radiology specialists (consultant work- force) in England went up 5% between 2012 and 2015, while in the same period the number of CT and MR scans increased by 29 and 26 percentage points respectively. In Scotland, the gap widened even further (The Royal College of Radiologists 2016). Today, the average radiologist is interpreting an image every three to four seconds, eight hours a day (Choi et al. 2016).Secondly, the image resolution of today’s scanners is continuously improving – resulting in an ever greater volume of data. Indeed, the estimated overall medical data volume doubles every three years, making it harder and harder for radiologists to make good use of the available information without extra help from computerized digital processing. It is desirable, both in radiological research and in clinical diagnostics, to be able to quantitatively analyze this largely unexploited wealth of data and, for example, utilize new measurable imaging biomarkers to assess disease progression and prognosis (O’Connor et al. 2017). Experts see considerable future potential in the transformation of radiology from a discipline of qualitative interpretation to one of quantita- tive analysis, which derives clinically relevant information from extensive data sets (“radiomics”). “Images are more than pictures, they are data,” American radiologist Robert Gillies and his colleagues write (Gillies et al. 2016). Of course, this direction for radiology will require powerful, automated procedures, some of which at least will come under the field of artificial intelligence.

This system integrator focuses on providing centralized gas pipeline monitoring systems for hospitals. The service they provide makes it possible for hospitals to reduce both maintenance and labor costs. Since hospitals may not have an existing network suitable for this type of system, GPRS communication provides an easy and ready-to-use solution for remote, distributed monitoring systemsSystem Requirements- GPRS communication - Seamless connection with SCADA software - Simple, front-end control capability - Expandable I/O channels - Combine AI, DI, and DO channels

Suppliers

An AI Signal Processing Engineer that identifies events, conditions and anomalies in signal and sensor data. Intended for use by R&D departments at companies creating devices and equipment instrumented with sensors, Reality AI Tools generates detection code that can be incorporated into our customer’s products, running either in the cloud, or at the edge on inexpensive, commodity hardware. Works with any type of sensor, alone or in sensor fusion. Best for higher sample-rate applications. Reality AI holds 10 patents and 6 patents-pending, all in the field of machine learning as applied to sensors and signals.

Unisound is mainly focus on Artificial Intelligence of smart sound recognition in the Internet of Things (IoT) field. The company is founded in 2012 Beijing, China with more than 200 employees around China.

Events

Organizations

The Association for the Advancement of Artificial Intelligence (AAAI) is an international, nonprofit, scientific society devoted to promote research in, and responsible use of, artificial intelligence.Founded in 1979, the Association for the Advancement of Artificial Intelligence (AAAI) (formerly the American Association for Artificial Intelligence) is a nonprofit scientific society devoted to advancing the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. AAAI aims to promote research in, and responsible use of, artificial intelligence. AAAI also aims to increase public understanding of artificial intelligence, improve the teaching and training of AI practitioners, and provide guidance for research planners and funders concerning the importance and potential of current AI developments and future directions.

iHive is located in a quality office suite in downtown San Diego and at the center of an exploding high technology (Qualcomm, Sony, AccelerateIT, AIS) and defense community (SPAWAR, BAE, General Dynamics NASSCO) and near San Diego State University. iHive members receive business support services from iHive’s experienced and highly skilled services team, robust and redundant data connectivity, shared reception area and conference rooms, and a strategic location along Freeway 5 and the nearby Enterprise Zone. iHive is just 5 minutes from San Diego Airport and less than 15 miles from Naval Air Stations Miramar and Coronado, US Navy Amphibious and USMC base Camp Pendleton. Members also have the opportunity to collaborate with the talented faculty and students of local highly ranked colleges and universities many graduating with technical degrees in computer science and IT.